
import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer


lowercase    :   Union[str, Any]						=			logging.get_logger(__name__)


lowercase    :   Tuple						=			{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

# See all MVP models at https://huggingface.co/models?filter=mvp
lowercase    :   Optional[int]						=			{
    'vocab_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json',
    },
    'added_tokens.json': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json',
    },
    'merges_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json',
    },
}

lowercase    :   Optional[Any]						=			{
    'RUCAIBox/mvp': 1024,
}




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	_A								=	VOCAB_FILES_NAMES
	_A								=	PRETRAINED_VOCAB_FILES_MAP
	_A								=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_A								=	['input_ids', 'attention_mask']
	_A								=	MvpTokenizer







	def __init__(   self						:Union[str, Any]	,  a						:Union[str, Any]=None	,  a						:int=None	,  a						:Union[str, Any]=None	,  a						:List[str]="replace"	,  a						:List[str]="<s>"	,  a						:str="</s>"	,  a						:Optional[Any]="</s>"	,  a						:Union[str, Any]="<s>"	,  a						:List[str]="<unk>"	,  a						:Dict="<pad>"	,  a						:Union[str, Any]="<mask>"	,  a						:Optional[Any]=False	,  a						:Optional[Any]=True	,  **a						:int	,  )						->					Tuple:
								super().__init__(
								    a	,  a	,  tokenizer_file=a	,  errors=a	,  bos_token=a	,  eos_token=a	,  sep_token=a	,  cls_token=a	,  unk_token=a	,  pad_token=a	,  mask_token=a	,  add_prefix_space=a	,  trim_offsets=a	,  **a	,  )

								__UpperCamelCase      :  int       =   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
								if pre_tok_state.get("add_prefix_space"	,  a		) != add_prefix_space:
															__UpperCamelCase      :  str       =   getattr(a	,  pre_tok_state.pop("type"		)		)
															__UpperCamelCase      :  Tuple       =   add_prefix_space
															__UpperCamelCase      :  Union[str, Any]       =   pre_tok_class(**a		)

								__UpperCamelCase      :  Optional[int]       =   add_prefix_space

								# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
								__UpperCamelCase      :  Tuple       =   "post_processor"
								__UpperCamelCase      :  Any       =   getattr(self.backend_tokenizer	,  a	,  a		)
								if tokenizer_component_instance:
															__UpperCamelCase      :  Tuple       =   json.loads(tokenizer_component_instance.__getstate__()		)

															# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
															if "sep" in state:
																						__UpperCamelCase      :  str       =   tuple(state["sep"]		)
															if "cls" in state:
																						__UpperCamelCase      :  Dict       =   tuple(state["cls"]		)

															__UpperCamelCase      :  Optional[Any]       =   False

															if state.get("add_prefix_space"	,  a		) != add_prefix_space:
																						__UpperCamelCase      :  Dict       =   add_prefix_space
																						__UpperCamelCase      :  int       =   True

															if state.get("trim_offsets"	,  a		) != trim_offsets:
																						__UpperCamelCase      :  int       =   trim_offsets
																						__UpperCamelCase      :  Any       =   True

															if changes_to_apply:
																						__UpperCamelCase      :  List[Any]       =   getattr(a	,  state.pop("type"		)		)
																						__UpperCamelCase      :  List[Any]       =   component_class(**a		)
																						setattr(self.backend_tokenizer	,  a	,  a		)







	@property
	def   _lowerCamelCase							(   self						:Optional[int]		)						->					str:
								if self._mask_token is None:
															if self.verbose:
																						logger.error("Using mask_token, but it is not set yet."		)
															return None
								return str(self._mask_token		)







	@mask_token.setter
	def   _lowerCamelCase							(   self						:Dict	,  a						:str		)						->					int:
								__UpperCamelCase      :  Dict       =   AddedToken(a	,  lstrip=a	,  rstrip=a		) if isinstance(a	,  a		) else value
								__UpperCamelCase      :  str       =   value







	def   _lowerCamelCase							(   self						:Optional[Any]	,  *a						:Optional[Any]	,  **a						:Tuple		)						->					BatchEncoding:
								__UpperCamelCase      :  Optional[Any]       =   kwargs.get("is_split_into_words"	,  a		)

								if is_split_into_words and not self.add_prefix_space:
															raise ValueError(
															    f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
															    "to use it with pretokenized inputs."		)

								return super()._batch_encode_plus(*a	,  **a		)







	def   _lowerCamelCase							(   self						:str	,  *a						:List[Any]	,  **a						:List[str]		)						->					BatchEncoding:
								__UpperCamelCase      :  Any       =   kwargs.get("is_split_into_words"	,  a		)

								if is_split_into_words and not self.add_prefix_space:
															raise ValueError(
															    f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
															    "to use it with pretokenized inputs."		)

								return super()._encode_plus(*a	,  **a		)







	def   _lowerCamelCase							(   self						:List[str]	,  a						:str	,  a						:Optional[str] = None		)						->					Tuple[str]:
								__UpperCamelCase      :  Optional[Any]       =   self._tokenizer.model.save(a	,  name=a		)
								return tuple(a		)







	def   _lowerCamelCase							(   self						:Tuple	,  a						:List[str]	,  a						:Tuple=None		)						->					List[str]:
								__UpperCamelCase      :  Union[str, Any]       =   [self.bos_token_id] + token_ids_a + [self.eos_token_id]
								if token_ids_a is None:
															return output

								return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







	def   _lowerCamelCase							(   self						:Union[str, Any]	,  a						:List[int]	,  a						:Optional[List[int]] = None		)						->					List[int]:
								__UpperCamelCase      :  Optional[int]       =   [self.sep_token_id]
								__UpperCamelCase      :  Union[str, Any]       =   [self.cls_token_id]

								if token_ids_a is None:
															return len(cls + token_ids_a + sep		) * [0]
								return len(cls + token_ids_a + sep + sep + token_ids_a + sep		) * [0]

from typing import List

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase    :   Union[str, Any]						=			logging.get_logger(__name__)

lowercase    :   Union[str, Any]						=			{
    'snap-research/efficientformer-l1-300': (
        'https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json'
    ),
}




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	_A								=	'efficientformer'







	def __init__(   self						:Union[str, Any]	,  a						:List[int] = [3, 2, 6, 4]	,  a						:List[int] = [4_8, 9_6, 2_2_4, 4_4_8]	,  a						:List[bool] = [True, True, True, True]	,  a						:int = 4_4_8	,  a						:int = 3_2	,  a						:int = 4	,  a						:int = 7	,  a						:int = 5	,  a						:int = 8	,  a						:int = 4	,  a						:float = 0.0	,  a						:int = 1_6	,  a						:int = 3	,  a						:int = 3	,  a						:int = 3	,  a						:int = 2	,  a						:int = 1	,  a						:float = 0.0	,  a						:int = 1	,  a						:bool = True	,  a						:bool = True	,  a						:float = 1E-5	,  a						:str = "gelu"	,  a						:float = 0.02	,  a						:float = 1E-1_2	,  a						:int = 2_2_4	,  a						:float = 1E-0_5	,  **a						:str	,  )						->					None:
								super().__init__(**a		)

								__UpperCamelCase      :  Optional[Any]       =   hidden_act
								__UpperCamelCase      :  Any       =   hidden_dropout_prob
								__UpperCamelCase      :  str       =   hidden_sizes
								__UpperCamelCase      :  Dict       =   num_hidden_layers
								__UpperCamelCase      :  Optional[Any]       =   num_attention_heads
								__UpperCamelCase      :  str       =   initializer_range
								__UpperCamelCase      :  List[str]       =   layer_norm_eps
								__UpperCamelCase      :  str       =   patch_size
								__UpperCamelCase      :  str       =   num_channels
								__UpperCamelCase      :  Any       =   depths
								__UpperCamelCase      :  Tuple       =   mlp_expansion_ratio
								__UpperCamelCase      :  List[str]       =   downsamples
								__UpperCamelCase      :  Optional[int]       =   dim
								__UpperCamelCase      :  Dict       =   key_dim
								__UpperCamelCase      :  Optional[Any]       =   attention_ratio
								__UpperCamelCase      :  Dict       =   resolution
								__UpperCamelCase      :  Union[str, Any]       =   pool_size
								__UpperCamelCase      :  Tuple       =   downsample_patch_size
								__UpperCamelCase      :  Optional[int]       =   downsample_stride
								__UpperCamelCase      :  Optional[int]       =   downsample_pad
								__UpperCamelCase      :  Union[str, Any]       =   drop_path_rate
								__UpperCamelCase      :  Union[str, Any]       =   num_metaad_blocks
								__UpperCamelCase      :  List[str]       =   distillation
								__UpperCamelCase      :  str       =   use_layer_scale
								__UpperCamelCase      :  Tuple       =   layer_scale_init_value
								__UpperCamelCase      :  str       =   image_size
								__UpperCamelCase      :  int       =   batch_norm_eps